import math
import torch
import torch.nn as nn
import torch.nn.functional as F


class BasicBlock(nn.Module):
    def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
        super(BasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_planes)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
                               padding=1, bias=False)
        self.droprate = dropRate
        self.equalInOut = (in_planes == out_planes)
        self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
                                                                padding=0, bias=False) or None

    def forward(self, x):
        if not self.equalInOut:
            x = self.relu1(self.bn1(x))
        else:
            out = self.relu1(self.bn1(x))
        if self.equalInOut:
            out = self.relu2(self.bn2(self.conv1(out)))
        else:
            out = self.relu2(self.bn2(self.conv1(x)))
        if self.droprate > 0:
            out = F.dropout(out, p=self.droprate, training=self.training)
        out = self.conv2(out)
        if not self.equalInOut:
            return torch.add(self.convShortcut(x), out)
        else:
            return torch.add(x, out)


class NetworkBlock(nn.Module):
    def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate)

    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate):
        layers = []
        for i in range(nb_layers):
            layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate))
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.layer(x)


class WideResNet(nn.Module):
    def __init__(self, depth, widen_factor=1, dropRate=0.0, normalizer=None, num_in_classes=10,
                 num_out_classes=0, num_v_classes=0, output_in_only=False):
        super(WideResNet, self).__init__()
        nChannels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
        assert ((depth - 4) % 6 == 0)
        n = (depth - 4) // 6
        block = BasicBlock
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1,
                               padding=1, bias=False)
        # 1st block
        self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
        # 2nd block
        self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
        # 3rd block
        self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(nChannels[3])
        self.relu = nn.ReLU(inplace=True)
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        num_classes = num_in_classes + num_out_classes + num_v_classes
        self.fc = nn.Linear(nChannels[3], num_classes)
        self.nChannels = nChannels[3]

        self.normalizer = normalizer
        self.num_in_classes = num_in_classes
        self.num_out_classes = num_out_classes
        self.num_v_classes = num_v_classes
        self.output_in_only = output_in_only

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def forward(self, x):
        if self.normalizer is not None:
            x = x.clone()
            x[:,0,:,:] = (x[:,0,:,:] - self.normalizer.mean[0]) / self.normalizer.std[0]
            x[:,1,:,:] = (x[:,1,:,:] - self.normalizer.mean[1]) / self.normalizer.std[1]
            x[:,2,:,:] = (x[:,2,:,:] - self.normalizer.mean[2]) / self.normalizer.std[2]

        out = self.conv1(x)
        out = self.block1(out)
        out = self.block2(out)
        out = self.block3(out)
        out = self.relu(self.bn1(out))
        # out = F.avg_pool2d(out, 8)
        out = self.avgpool(out)
        out = out.view(-1, self.nChannels)
        if self.output_in_only:
            return self.fc(out)[:, :self.num_in_classes]
        else:
            return self.fc(out)

    # function to extact the multiple features
    def feature_list(self, x):
        if self.normalizer is not None:
            x = x.clone()
            x[:,0,:,:] = (x[:,0,:,:] - self.normalizer.mean[0]) / self.normalizer.std[0]
            x[:,1,:,:] = (x[:,1,:,:] - self.normalizer.mean[1]) / self.normalizer.std[1]
            x[:,2,:,:] = (x[:,2,:,:] - self.normalizer.mean[2]) / self.normalizer.std[2]

        out_list = []

        out = self.conv1(x)
        out_list.append(out)
        out = self.block1(out)
        out_list.append(out)
        out = self.block2(out)
        out_list.append(out)
        out = self.block3(out)
        out_list.append(out)
        out = self.relu(self.bn1(out))
        # out = F.avg_pool2d(out, 8)
        out = self.avgpool(out)
        out = out.view(-1, self.nChannels)

        y = self.fc(out)

        return y, out_list

    # function to extact a specific feature
    def intermediate_forward(self, x, layer_index):
        if self.normalizer is not None:
            x = x.clone()
            x[:,0,:,:] = (x[:,0,:,:] - self.normalizer.mean[0]) / self.normalizer.std[0]
            x[:,1,:,:] = (x[:,1,:,:] - self.normalizer.mean[1]) / self.normalizer.std[1]
            x[:,2,:,:] = (x[:,2,:,:] - self.normalizer.mean[2]) / self.normalizer.std[2]

        out = self.conv1(x)
        if layer_index == 1:
            out = self.block1(out)
        elif layer_index == 2:
            out = self.block1(out)
            out = self.block2(out)
        elif layer_index == 3:
            out = self.block1(out)
            out = self.block2(out)
            out = self.block3(out)

        return out

    # function to extact the penultimate features
    def penultimate_forward(self, x):
        if self.normalizer is not None:
            x = x.clone()
            x[:,0,:,:] = (x[:,0,:,:] - self.normalizer.mean[0]) / self.normalizer.std[0]
            x[:,1,:,:] = (x[:,1,:,:] - self.normalizer.mean[1]) / self.normalizer.std[1]
            x[:,2,:,:] = (x[:,2,:,:] - self.normalizer.mean[2]) / self.normalizer.std[2]

        out = self.conv1(x)
        out = self.block1(out)
        out = self.block2(out)
        penultimate = self.block3(out)
        out = self.relu(self.bn1(out))
        # out = F.avg_pool2d(out, 8)
        out = self.avgpool(out)
        out = out.view(-1, self.nChannels)
        y = self.fc(out)

        return y, penultimate
